arXiv 18 Oct 2019 · Econometrics · publishedJournal of Econometrics (2022) · 63 citations (OpenAlex)
arXiv:1910.08273 · PDF · DOI · OpenAlex · Extracted main text
This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We propose an easy-to-use all-purpose estimator for a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under an approximate factor model and general missing patterns. The key application is to estimate counterfactual outcomes in causal inference from panel data. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment effects at any time under general adoption patterns where the units can be affected by unobserved factors.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bai and Ng (2002) Determining the number of factors in approximate factor models | 0.956 | 8 | 6 | 88% |
| 2 | Bai (2003) Inferential theory for factor models of large dimensions | 0.874 | 15 | 7 | 67% |
| 3 | Kang and Schafer (2007) Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data | 0.843 | 3 | 3 | 100% |
| 4 | Bai and Ng (2021) Matrix completion, counterfactuals, and factor analysis of missing data | 0.811 | 30 | 5 | 53% |
| 5 | Cahan, Bai, and Ng (2021) Factor-Based Imputation of Missing Values and Covariances in Panel Data of Large Dimensions | 0.737 | 3 | 2 | 100% |
| 6 | Jin, Miao, and Su (2021) On factor models with random missing: EM estimation, inference, and cross validation | 0.669 | 60 | 6 | 30% |
| 7 | Abadie, Diamond, and Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program | 0.644 | 2 | 2 | 100% |
| 8 | Abadie, Diamond, and Hainmueller (2015) Comparative politics and the synthetic control method | 0.644 | 2 | 2 | 100% |
| 9 | Athey and Imbens (2021) Design-based analysis in difference-in-differences settings with staggered adoption | 0.644 | 2 | 2 | 100% |
| 10 | Athey, Bayati, Doudchenko, Imbens, and Khosravi (2021) Matrix completion methods for causal panel data models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 57 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.